US20220002810A1

US20220002810A1 - Methods and compositions for correlating genetic markers with prostate cancer risk

Info

Publication number: US20220002810A1
Application number: US17/177,568
Authority: US
Inventors: Jianfeng Xu; S. Lilly Zheng; Henrik Grönberg; William B. Isaacs
Original assignee: Karolinska Institutet Innovations AB; Johns Hopkins University; Wake Forest University Health Sciences
Current assignee: Karolinska Institutet Innovations AB; Johns Hopkins University; Wake Forest University Health Sciences
Priority date: 2007-12-21
Filing date: 2021-02-17
Publication date: 2022-01-06
Also published as: WO2009085196A8; US20180208994A1; WO2009085196A1; US20090226912A1

Abstract

The present invention provides a method of identifying a subject as having an increased risk of developing prostate cancer, comprising detecting in the subject the presence of various polymorphisms associated with an increased risk of developing prostate cancer.

Description

STATEMENT OF PRIORITY

This application is a continuation of U.S. application Ser. No. 15/863,636, filed Jan. 5, 2018, which is a continuation of U.S. application Ser. No. 12/339,653, filed Dec. 19, 2008, now abandoned, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application Ser. No. 61/016,117, filed Dec. 21, 2007, the entire contents of each of which are incorporated by reference herein.

GOVERNMENT SUPPORT

This invention was made with government support under grant #CA106523, CA95052, CA1125117, and CA58236 awarded by the National Institutes of Health and grant #PC051264 awarded by the Department of Defense. The government has certain rights in the invention.

STATEMENT REGARDING ELECTRONIC FILING OF A SEQUENCE LISTING

A Sequence Listing in ASCII text format, submitted under 37 C.F.R. § 1.821, entitled 9151-107TSCT2 ST25.txt, 9,934 bytes in size, generated on Feb. 10, 2021 and filed via EFS-Web, is provided in lieu of a paper copy. This Sequence Listing is hereby incorporated by reference herein into the specification for its disclosures.

FIELD OF THE INVENTION

The present invention provides methods and compositions directed to identification of genetic markers associated with prostate cancer.

BACKGROUND OF THE INVENTION

Genome-wide association (GWA) studies have identified sequence variants that are consistently associated with risk for complex diseases¹. Such variants have limited utility in the assessment of disease risk in an individual, however, because most of them confer a relatively small risk. What is needed is a determination of whether combinations of individual variants confer larger, more clinically useful, increases in risk.
Age, race, and family history are the three risk factors that are consistently associated with the risk of prostate cancer³. A meta analysis found a pooled odds ratio of 2.5 for men who have an affected first-degree relative⁴. In the present invention, genetic variants in five chromosomal regions associated with a statistically significant risk of prostate cancer have been identified using genome-wide analysis. These include three independent regions at 8q24^5-8and one region each at 17q12 and 17q24.3⁹. While it is anticipated that these five regions harbor prostate cancer susceptibility genes or regulatory factors affecting critical genes, the specific genes in question have not been identified to date.
Thus, the present invention overcomes previous shortcomings in the art by identifying significant statistical associations between a combination of genetic markers in different chromosomal regions and prostate cancer. Thus, the present invention provides methods and compositions for identifying a subject at increased risk of developing prostate cancer by detecting the genetic markers of this invention.

SUMMARY OF THE INVENTION

In one aspect, the present invention provides a method of identifying a subject as having an increased risk of developing prostate cancer, comprising detecting in nucleic acid of the subject the presence of two or more polymorphisms associated with an increased risk of prostate cancer, wherein each of the two or more polymorphisms is present in a different chromosome region selected from the group consisting of:

- a) chromosome region 17q12;
- b) chromosome region 17q24.3;
- c) chromosome region 8q24 (Region 2);
- d) 8q24 (Region 3);
- e) and 8q24 (Region 1); and
- f) any combination of (a)-(e) above,
  whereby the presence of said two or more polymorphisms identifies the subject as having an increased risk of developing prostate cancer. It is further provided that the methods of this invention comprise detecting three or more polymorphisms associated with an increased risk of prostate cancer, each from a different chromosome region among those listed as (a)-(e) above, in any combination; detecting four or more polymorphisms associated with an increased risk of prostate cancer, each from a different chromosome region among those listed as (a)-(e) above, in any combination; and/or detecting five polymorphisms associated with increased risk of prostate cancer, each from a different chromosome region among those listed as (a)-(e) above. The two, three, four or five polymorphisms can also be detected in combination with other polymorphisms associated with increased risk of prostate cancer, which can be present in the chromosome regions listed as (a)-(e) above (e.g., in linkage disequilibrium) and/or which can be present in other chromosome regions in which polymorphisms associated with increased prostate cancer risk are known or later identified to be present.

The methods of the present invention can also be employed in identifying a subject having an increased risk of developing prostate cancer by detecting the various polymorphisms and genetic markers described herein and further identifying a family history of prostate cancer in the subject, whereby the presence of any of the combinations of risk markers in the subject's genotypic makeup as described herein and a family history of prostate cancer identify the subject as having an increased risk of developing prostate cancer. The methods of this invention can also be used to supplement the predictive value of prostate serum antigen (PSA). Thus, a subject having any of the combinations of risk markers as described herein and an elevated and/or rising PSA serum level is a subject that has an increased risk of developing prostate cancer.
In a further aspect, the present invention provides a method of identifying a human subject as having an increased risk of developing prostate cancer, comprising detecting in the subject the presence of two or more alleles selected from the group consisting of:

- a) the T allele of single nucleotide polymorphism rs4430796;
- b) the G allele of single nucleotide polymorphism rs1859962;
- c) the A allele of single nucleotide polymorphism rs16901979;
- d) the G allele of single nucleotide polymorphism rs6983267;
- e) the A allele of single nucleotide polymorphism rs1447295; and
- f) any combination of (a), (b), (c) (d) and (e) above,
  whereby the presence of said alleles identifies the subject as having an increased risk of developing prostate cancer. Thus, the methods of this invention can comprise detecting three or more alleles among those listed as (a)-(e) above, in any combination; detecting four or more alleles among those listed as (a)-(e) above, in any combination; and/or detecting all five of the alleles listed as (a)-(e) above. The two, three, four or five alleles can also be detected in combination with other alleles and/or polymorphisms, which can be present in any of the chromosome regions in which the alleles of (a)-(e) above are located (e.g., in linkage disequilibrium with any of the alleles of (a)-(e) above) and/or which can be present in other chromosome regions in which alleles associated with prostate cancer risk are known or later identified to be present.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is explained in greater detail below. This description is not intended to be a detailed catalog of all the different ways in which the invention may be implemented, or all the features that may be added to the instant invention. For example, features illustrated with respect to one embodiment may be incorporated into other embodiments, and features illustrated with respect to a particular embodiment may be deleted from that embodiment. In addition, numerous variations and additions to the various embodiments suggested herein will be apparent to those skilled in the art in light of the instant disclosure, which do not depart from the instant invention. Hence, the following specification is intended to illustrate some particular embodiments of the invention, and not to exhaustively specify all permutations, combinations and variations thereof.
The present invention is based on the unexpected discovery that the combination of alleles in various chromosome regions is statistically associated with an increased risk of developing prostate cancer. There are numerous benefits of carrying out the methods of this invention to identify a subject having an increased risk of developing prostate cancer, including but not limited to, identifying subjects who are good candidates for prophylactic and/or therapeutic treatment, and screening for cancer at an earlier time or more frequently than might otherwise be indicated, to increase the chances of early detection of a prostate cancer. Thus, in one aspect, the present invention provides a method of identifying a subject (e.g., a human subject) as having an increased risk of developing prostate cancer, comprising detecting in nucleic acid of the subject the presence of two or more polymorphisms, wherein each of the two or more polymorphisms is present in a different chromosome region selected from the group consisting of:

- a) chromosome region 17q12;
- b) chromosome region 17q24.3;
- c) chromosome region 8q24 (Region 2);
- d) 8q24 (Region 3);
- e) and 8q24 (Region 1); and
- f) any combination of (a)-(e) above,
  whereby the presence of said two or more polymorphisms identifies the subject as having an increased risk of developing prostate cancer.

As noted herein, the methods of this invention can comprise detecting three or more polymorphisms, each from a different chromosome region among those listed as (a)-(e) above, in any combination; detecting four or more polymorphisms, each from a different chromosome region among those listed as (a)-(e) above, in any combination; and/or detecting five polymorphisms, each from a different chromosome region among those listed as (a)-(e) above.
Thus, the present invention provides methods for detection of a polymorphism or genetic marker of this invention in any of the following combinations of chromosome regions, wherein a, b, c, d and e represent each chromosome region as listed herein.
Combinations of two alleles include: a and b; a and c; a and d; a and e; b and c; b and d; b and e; c and d; c and e; d and e.
Combinations of three alleles include: a, b and c; a, b and d; a, b and e; a, c and e; a, c and d; a, e and d; b, c and d; b, c and e; b, d and e; c, d and e.
Combinations of four alleles include: a, b, c and d; a, b, c and e; b, c, d and e; a, b, c and e; a, c, d and e; and a, b, d and e.
The two, three, four or five polymorphisms can also be detected in combination with other polymorphisms, present in any one two, three, four or five of the chromosome regions listed as (a)-(e) above and/or present in other chromosome regions in which polymorphisms and genetic markers associated with prostate cancer risk are known or later identified to be present.
In certain embodiments of this invention, the polymorphism in chromosome region 17q12 can be the T allele of the single nucleotide polymorphism having GenBank® database Accession No. rs4430796. In other embodiments, the polymorphism in chromosome region 17q24.3 can be the G allele of the single nucleotide polymorphism having GenBank® database Accession No. rs1859962. In further embodiments, the polymorphism in chromosome region 8q24 (Region 1) can be the A allele of the single nucleotide polymorphism having GenBank® database Accession No. rs1447295. In still further embodiments, the polymorphism in chromosome region 8q24 (Region 2) can be the A allele of the single nucleotide polymorphism having GenBank® database Accession No. rs16901979. In other embodiments, the polymorphism in chromosome region 8124 (Region 3) can be the G allele of the single nucleotide polymorphism having GenBank® database Accession No. rs6983267.
In a further aspect, the present invention provides a method of identifying a human subject as having an increased risk of developing prostate cancer, comprising detecting in the subject the presence of two or more alleles selected from the group consisting of:
a) the T allele of the single nucleotide polymorphism having GenBank® database Accession No. rs4430796;
b) the G allele of the single nucleotide polymorphism having GenBank® database Accession No. rs1859962;
c) the A allele of the single nucleotide polymorphism having GenBank® database Accession No. rs16901979;
d) the G allele of the single nucleotide polymorphism having GenBank® database Accession No. rs6983267;
e) the A allele of the single nucleotide polymorphism having GenBank® database Accession No. rs1447295; and
f) any combination of (a), (b), (c) (d) and (e) above,
whereby the presence of said alleles identifies the subject as having an increased risk of developing prostate cancer. Thus, the methods of this invention can further comprise detecting, in a subject, three or more alleles among those listed as (a)-(e) above, in any combination; detecting four or more alleles among those listed as (a)-(e) above, in any combination; and/or detecting all five of the alleles listed as (a)-(e) above. The two, three, four or five alleles can also be detected in combination with other alleles, which can be present in the chromosome regions in which the alleles of (a)-(e) above are located and/or which can be present in other chromosome regions in which alleles associated with prostate cancer risk are known or later identified to be present.
Thus, for example, the following combinations of alleles can be detected according to the methods of this invention to identify a subject as having an increased risk of developing prostate cancer, wherein a, b, c, d and e represent each of the alleles as listed herein.
Combinations of two alleles can include: a and b; a and c; a and d; a and e; b and c; b and d; b and e; c and d; c and e; d and e.
Combinations of three alleles can include: a, b and c; a, b and d; a, b and e; a, c and e; a, c and d; a, e and d; b, c and d; b, c and e; b, d and e; c, d and e.
Combinations of four alleles include: a, b, c and d; a, b, c and e; b, c, d and e; a, b, c and e; a, c, d and e; and a, b, d and e.
Additional risk alleles that can be detected in the methods of this invention to identify a subject as having an increased risk of developing prostate cancer, with and without a family history of prostate cancer and/or with and without an elevated and/or rising PSA level are described in Tables 8-12 herein. These alleles can be present in any combination with any of the five alleles described above as (a)-(e) and/or in any combination with one another.
The present invention further provides embodiments wherein a subject of this invention is heterozygous for an allele of this invention and other embodiments wherein a subject of this invention is homozygous for an allele of this invention. In the methods provided herein wherein a combination of alleles is analyzed, the subject can be heterozygous or homozygous for any given allele in any combination relative to the other alleles in the combination.
In certain embodiments of this invention, the methods described herein can be employed to identify 1) a subject at increased or decreased risk of a more aggressive form of prostate cancer (e.g., having a Gleason score of 7 (4+3) to 10), 2) a subject at increased or decreased risk of a poor prognosis (e.g., increased likelihood the cancer will metastasize, will be poorly responsive to treatment and/or will lead to death) once cancer has been diagnosed in the subject; and/or 3) a subject at increased or decreased risk of an early age of onset of prostate cancer, by identifying in the subject the polymorphisms and/or alleles of this invention.
It is further contemplated that the methods of this invention can be carried out to diagnose prostate cancer in a subject, by detecting the combinations of polymorphisms or genetic markers described herein.
In further aspects, the present invention provides a kit for carrying out the methods of this invention, wherein the kit can comprise primers, probes, primer/probe sets, reagents, buffers, etc., as would be known in the art, for the detection of the polymorphisms and/or alleles of this invention in a nucleic acid sample from a subject. For example, a primer or probe can comprise a contiguous nucleotide sequence that is complementary to a region comprising a polymorphism or genetic marker of this invention. In particular embodiments, a kit of this invention will comprise primers and probes that allow for the specific detection of the polymorphisms and genetic markers of this invention. Such a kit can further comprise blocking probes, labeling reagents, blocking agents, restriction enzymes, antibodies, sampling devices, positive and negative controls, etc., as would be well known to those of ordinary skill in the art.

Definitions

As used herein, “a,” “an” or “the” can mean one or more than one. For example, “a” cell can mean a single cell or a multiplicity of cells.
Also as used herein, “and/or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (“or”).
Furthermore, the term “about,” as used herein when referring to a measurable value such as an amount of a compound or agent of this invention, dose, time, temperature, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, ±0.5%, or even ±0.1% of the specified amount.
As used herein, the term “prostate cancer” describes an uncontrolled (malignant) growth of cells in the prostate gland, which is located at the base of the urinary bladder and is responsible for helping control urination as well as forming part of the semen. Symptoms of prostate cancer can include, but are not limited to, urinary problems (e.g., not being able to urinate; having a hard time starting or stopping the urine flow; needing to urinate often, especially at night; weak flow of urine; urine flow that starts and stops; pain or burning during urination), difficulty having an erection, blood in the urine or semen, and/or frequent pain in the lower back, hips, or upper thighs.
The term “chromosome region” as used herein refers to a part of a chromosome defined either by anatomical details, especially by banding, or by its linkage groups. The particular chromosome regions of this invention are further defined by the following boundaries.
Chromosome region 17q12: Region around rs4430796 (chr17:33,172,153): from 33,163,028 to 33,189,279, ˜20 Kb, #SNPs=11 (Table 8).
Chromosome region 17q24.2: Region around rs1859962 (chr17:66,20,348): from 66,616,533 to 66,754,527, ˜140 Kb, #SNPs=174 (Table 9).
Chromosome region 8q24 (Region 2): Region around rs16901979 (chr8:128,194,098): from 128,145,397 to 128,215,780), ˜70 kb, #SNPs=112 (Table 11).
Chromosome region 8q24 (Region 3): Region around rs6983267 (chr8:128,482,487): from 128,469,358 to 128,535,996, ˜65 kb, #SNPs=70 (Table 12).
Chromosome region 8q24 (Region 1): Region around rs1447295 (chr8:128,554,220): from 128,536,936 to 128,617,860, ˜80 kb, #SNPs=116 (Table 10).
All the positions described above are based on Build 35 and the SNPs are based on Hapmap SNP release 21.
Also as used herein, “linked” describes a region of a chromosome that is shared more frequently in family members or members of a population manifesting a particular phenotype and/or affected by a particular disease or disorder, than would be expected or observed by chance, thereby indicating that the gene or genes or other identified marker(s) within the linked chromosome region contain or are associated with an allele that is correlated with the phenotype and/or presence of a disease or disorder, or with an increased or decreased likelihood of the phenotype and/or of the disease or disorder. Once linkage is established, association studies (linkage disequilibrium) can be used to narrow the region of interest or to identify the marker (e.g., allele or haplotype) correlated with the phenotype and/or disease or disorder.
Furthermore, as used herein, the term “linkage disequilibrium” or “LD” refers to the occurrence in a population of two linked alleles at a frequency higher or lower than expected on the basis of the gene frequencies of the individual genes. Thus, linkage disequilibrium describes a situation where alleles occur together more often than can be accounted for by chance, which indicates that the two alleles are physically close on a DNA strand.
The term “genetic marker” or “polymorphism” as used herein refers to a characteristic of a nucleotide sequence (e.g., in a chromosome) that is identifiable due to its variability among different subjects (i.e., the genetic marker or polymorphism can be a single nucleotide polymorphism, a restriction fragment length polymorphism, a microsatellite, a deletion of nucleotides, an addition of nucleotides, a substitution of nucleotides, a repeat or duplication of nucleotides, a translocation of nucleotides, and/or an aberrant or alternate splice site resulting in production of a truncated or extended form of a protein, etc., as would be well known to one of ordinary skill in the art).
A “single nucleotide polymorphism” (SNP) in a nucleotide sequence is a genetic marker that is polymorphic for two (or in some case three or four) alleles. SNPs can be present within a coding sequence of a gene, within noncoding regions of a gene and/or in an intergenic (e.g., intron) region of a gene. A SNP in a coding region in which both forms lead to the same polypeptide sequence is termed synonymous (i.e., a silent mutation) and if a different polypeptide sequence is produced, the alleles of that SNP are non-synonymous. SNPs that are not in protein coding regions can still have effects on gene splicing, transcription factor binding and/or the sequence of non-coding RNA.
The SNP nomenclature provided herein refers to the official Reference SNP (rs) identification number as assigned to each unique SNP by the National Center for Biotechnological Information (NCBI), which is available in the GenBank® database.
In some embodiments, the term genetic marker is also intended to describe a phenotypic effect of an allele or haplotype, including for example, an increased or decreased amount of a messenger RNA, an increased or decreased amount of protein, an increase or decrease in the copy number of a gene, production of a defective protein, tissue or organ, etc., as would be well known to one of ordinary skill in the art.
An “allele” as used herein refers to one of two or more alternative forms of a nucleotide sequence at a given position (locus) on a chromosome. Usually alleles are nucleotides present in a nucleotide sequence that makes up the coding sequence of a gene, but sometimes the term is used to refer to a nucleotide in a non-coding region of a gene. An individual's genotype for a given gene is the set of alleles it happens to possess. As noted herein, an individual can be heterozygous or homozygous for an allele of this invention.
Also as used herein, a “haplotype” is a set of SNPs on a single chromatid that are statistically associated. It is thought that these associations, and the identification of a few alleles of a haplotype block, can unambiguously identify all other polymorphic sites in its region. The term “haplotype” is also commonly used to describe the genetic constitution of individuals with respect to one member of a pair of allelic genes; sets of single alleles or closely linked genes that tend to be inherited together.
The terms “increased risk” and “decreased risk” as used herein define the level of risk that a subject has of developing prostate cancer, as compared to a control subject that does not have the polymorphisms and genetic markers of this invention in the control subject's nucleic acid.
A sample of this invention can be any sample containing nucleic acid of a subject, as would be well known to one of ordinary skill in the art. Nonlimiting examples of a sample of this invention include a cell, a body fluid, a tissue, a washing, a swabbing, etc., as would be well known in the art.
A subject of this invention is any animal that is susceptible to prostate cancer as defined herein and can include, for example, humans, as well as animal models of prostate cancer (e.g., rats, mice, dogs, nonhuman primates, etc.). In some aspects of this invention, the subject can be a Caucasian (e.g., white; European-American; Hispanic) human and in other aspects the subject can be a human of black African ancestry (e.g., black; African American; African-European; African-Caribbean, etc.). In yet other aspects the subject can be Asian. In further aspects of this invention, the subject has a family history of prostate cancer (e.g., having at least one first degree relative diagnosed with prostate cancer) and in some embodiments, the subject does not have a family history of prostate cancer. Additionally a subject of this invention has a diagnosis of prostate cancer in certain embodiments and in other embodiments, a subject of this invention does not have a diagnosis of prostate cancer.
As used herein, “nucleic acid” encompasses both RNA and DNA, including cDNA, genomic DNA, mRNA, synthetic (e.g., chemically synthesized) DNA and chimeras, fusions and/or hybrids of RNA and DNA. The nucleic acid can be double-stranded or single-stranded. Where single-stranded, the nucleic acid can be a sense strand or an antisense strand. The nucleic acid can be synthesized using oligonucleotide analogs or derivatives (e.g., inosine or phosphorothioate nucleotides, etc.). Such oligonucleotides can be used, for example, to prepare nucleic acids that have altered base-pairing abilities or increased resistance to nucleases.
An “isolated nucleic acid” is a nucleotide sequence that is not immediately contiguous with nucleotide sequences with which it is immediately contiguous (one on the 5′ end and one on the 3′ end) in the naturally occurring genome of the organism from which it is derived. Thus, in one embodiment, an isolated nucleic acid includes some or all of the 5′ non-coding (e.g., promoter) sequences that are immediately contiguous to a coding sequence. The term therefore includes, for example, a recombinant DNA that is incorporated into a vector, into an autonomously replicating plasmid or virus, or into the genomic DNA of a prokaryote or eukaryote, or which exists as a separate molecule (e.g., a cDNA or a genomic DNA fragment produced by PCR or restriction endonuclease treatment), independent of other sequences. It also includes a recombinant DNA that is part of a hybrid nucleic acid encoding an additional polypeptide or peptide sequence.
The term “isolated” can refer to a nucleic acid or polypeptide that is substantially free of cellular material, viral material, and/or culture medium (e.g., when produced by recombinant DNA techniques), or chemical precursors or other chemicals (when chemically synthesized). Moreover, an “isolated fragment” is a fragment of a nucleic acid or polypeptide that is not naturally occurring as a fragment and would not be found in the natural state.
The term “oligonucleotide” refers to a nucleic acid sequence of at least about six nucleotides to about 100 nucleotides, for example, about 15 to about 30 nucleotides, or about 20 to about 25 nucleotides, which can be used, for example, as a primer in a PCR amplification and/or as a probe in a hybridization assay or in a microarray. Oligonucleotides of this invention can be natural or synthetic, e.g., DNA, RNA, PNA, LNA, modified backbones, etc., as are well known in the art.
The present invention further provides fragments of the nucleic acids of this invention, which can be used, for example, as primers and/or probes. Such fragments or oligonucleotides can be detectably labeled or modified, for example, to include and/or incorporate a restriction enzyme cleavage site when employed as a primer in an amplification (e.g., PCR) assay.
The detection of a polymorphism, genetic marker or allele of this invention can be carried out according to various protocols standard in the art and as described herein for analyzing nucleic acid samples and nucleotide sequences, as well as identifying specific nucleotides in a nucleotide sequence.
For example, nucleic acid can be obtained from any suitable sample from the subject that will contain nucleic acid and the nucleic acid can then be prepared and analyzed according to well-established protocols for the presence of genetic markers according to the methods of this invention. In some embodiments, analysis of the nucleic acid can be carried by amplification of the region of interest according to amplification protocols well known in the art (e.g., polymerase chain reaction, ligase chain reaction, strand displacement amplification, transcription-based amplification, self-sustained sequence replication (3 SR), Qβ replicase protocols, nucleic acid sequence-based amplification (NASBA), repair chain reaction (RCR) and boomerang DNA amplification (BDA), etc.). The amplification product can then be visualized directly in a gel by staining or the product can be detected by hybridization with a detectable probe. When amplification conditions allow for amplification of all allelic types of a genetic marker, the types can be distinguished by a variety of well-known methods, such as hybridization with an allele-specific probe, secondary amplification with allele-specific primers, by restriction endonuclease digestion, and/or by electrophoresis. Thus, the present invention further provides oligonucleotides for use as primers and/or probes for detecting and/or identifying genetic markers according to the methods of this invention.
The genetic markers of this invention are correlated with (i.e., identified to be statistically associated with) prostate cancer as described herein according to methods well known in the art and as disclosed in the Examples provided herein for statistically correlating genetic markers with various phenotypic traits, including disease states and pathological conditions as well as determining levels of risk associated with developing a particular phenotype, such as a disease or pathological condition. In general, identifying such correlation involves conducting analyses that establish a statistically significant association and/or a statistically significant correlation between the presence of a genetic marker or a combination of markers and the phenotypic trait in a population of subjects and controls (e.g., ethnically matched controls). The correlation can involve one or more than one genetic marker of this invention (e.g., two, three, four, five, or more) in any combination. An analysis that identifies a statistical association (e.g., a significant association) between the marker or combination of markers and the phenotype establishes a correlation between the presence of the marker or combination of markers in a population of subjects and the particular phenotype being analyzed. A level of risk (e.g., increased or decreased) can then be determined for an individual on the basis of such population-based analyses.
Thus, in certain embodiments, the present invention provides a method of screening a subject for polymorphisms that are associated with prostate cancer, comprising: a) performing a population based study to detect polymorphisms in a group of subjects with prostate cancer and ethnically matched controls; b) identifying polymorphisms in the group of subjects that are statistically associated with prostate cancer; and c) screening a subject for the presence of the polymorphisms identified in step (b).
The present invention further provides a method of identifying an effective and/or appropriate (i.e., for a given subject's particular condition or status) treatment regimen for a subject with prostate cancer, comprising detecting one or more of the polymorphisms and genetic markers associated with prostate cancer of this invention in the subject, wherein the one or more polymorphisms and genetic markers are further statistically correlated with an effective and/or appropriate treatment regimen for prostate cancer according to protocols as described herein and as are well known in the art.
Also provided is a method of identifying an effective and/or appropriate treatment regimen for a subject with prostate cancer, comprising: a) correlating the presence of one or more genetic markers of this invention in a test subject or population of test subjects with prostate cancer for whom an effective and/or appropriate treatment regimen has been identified; and b) detecting the one or more markers of step (a) in the subject, thereby identifying an effective and/or appropriate treatment regimen for the subject.
Further provided is a method of correlating a polymorphism or genetic marker of this invention with an effective and/or appropriate treatment regimen for prostate cancer, comprising: a) detecting in a subject or a population of subjects with prostate cancer and for whom an effective and/or appropriate treatment regimen has been identified, the presence of one or more genetic markers or polymorphisms of this invention; and b) correlating the presence of the one or more genetic markers of step (a) with an effective treatment regimen for prostate cancer.
Examples of treatment regimens for prostate cancer are well known in the art. Subjects who respond well to particular treatment protocols can be analyzed for specific genetic markers and a correlation can be established according to the methods provided herein. Alternatively, subjects who respond poorly to a particular treatment regimen can also be analyzed for particular genetic markers correlated with the poor response. Then, a subject who is a candidate for treatment for prostate cancer can be assessed for the presence of the appropriate genetic markers and the most effective and/or appropriate treatment regimen can be provided.
In some embodiments, the methods of correlating genetic markers with treatment regimens of this invention can be carried out using a computer database. Thus the present invention provides a computer-assisted method of identifying a proposed treatment for prostate cancer. The method involves the steps of (a) storing a database of biological data for a plurality of subjects, the biological data that is being stored including for each of said plurality of subjects, for example, (i) a treatment type, (ii) at least one genetic marker associated with prostate cancer and (iii) at least one disease progression measure for prostate cancer from which treatment efficacy can be determined; and then (b) querying the database to determine the dependence on said genetic marker of the effectiveness of a treatment type in treating prostate cancer, to thereby identify a proposed treatment as an effective and/or appropriate treatment for a subject carrying a genetic marker correlated with prostate cancer.
In one embodiment, treatment information for a subject is entered into the database (through any suitable means such as a window or text interface), genetic marker information for that subject is entered into the database, and disease progression information is entered into the database. These steps are then repeated until the desired number of subjects has been entered into the database. The database can then be queried to determine whether a particular treatment is effective for subjects carrying a particular marker or combination of markers, not effective for subjects carrying a particular marker or combination of markers, etc. Such querying can be carried out prospectively or retrospectively on the database by any suitable means, but is generally done by statistical analysis in accordance with known techniques, as described herein.
The present invention is more particularly described in the following examples that are intended as illustrative only since numerous modifications and variations therein will be apparent to those skilled in the art.

EXAMPLES

Example 1 Cumulative Effect of SNPs in the Five Chromosomal Regions of this Invention on Prostate Cancer Risk in a Caucasian Population

Study Sample

The study sample was described in detail elsewhere¹⁰. Briefly, a large-scale population-based case-control study was conducted in Sweden, named CAPS (CAncer Prostate in Sweden). Prostate cancer patients were identified and recruited from four of the six regional cancer registries in Sweden. The inclusion criterion for case subjects was pathological or cytological verified adenocarcinoma of the prostate, diagnosed between July, 2001 and October, 2003. Among 3,648 identified prostate cancer case subjects, 3,161 (87%) agreed to participate. DNA samples from blood and TNM stage, Gleason grade (biopsy), and PSA levels at diagnosis were available for 2,893 patients (91%). These case subjects were classified as having advanced disease if they met any of the following criteria: T3/4, N+, M+, Gleason score sum ≥8, or PSA>50 ng/ml; otherwise, they were classified as localized. Control subjects were recruited concurrently with case subjects. They were randomly selected from the Swedish Population Registry, and matched according to the expected age distribution of cases (groups of five-year intervals) and geographical region. A total of 3,153 controls were invited and 2,149 (68%) agreed to participate. DNA samples from blood were available for 1,781 control subjects (83%). Serum PSA level was measured for all control subjects but was not used as an exclusion variable. A history of prostate cancer among first-degree relatives was obtained from a questionnaire for both cases and controls. Table 1 presents the demographic and clinical characteristics of the study subjects, which were Caucasian. Recruitment of the study population was completed in two phases, each with a similar number of subjects, those before Oct. 31, 2002 (CAPS1) and after Nov. 1, 2002 (CAPS2). Each participant gave written informed consent. The study received institutional approval at the Karolinska Institute, Umea University, and Wake Forest University School of Medicine.

Selection of SNPs and SNP Genotyping

Sixteen SNPs from five chromosomal regions (three at 8q24 and one each at 17q12 and 17q24.3) that have been reported to be associated with prostate cancer^7-9,11were selected for this study. Polymerase chain reaction (PCR) and extension primers for these SNPs were designed using the MassARRAY Assay Design 3.0 software (Sequenom, Inc). The primer information is shown in Table 13. PCR and extension reactions were performed according to the manufacturer's instructions, and extension product sizes were determined by mass spectrometry using the Sequenom iPLEX system. Duplicate test samples and two water samples (PCR negative controls) that were blinded to the technician were included in each 96-well plate. The rate of concordant results between duplicate samples was >99%.

Statistical Analyses

Tests for Hardy-Weinberg equilibrium were performed for each SNP separately among case patients and control subjects using Fisher's exact test. Pair-wise linkage disequilibrium (LD) was tested for SNPs within each of the five chromosomal regions in control subjects using SAS/Genetics software (Version 9.0).
Allele frequency differences between case patients and control subjects were tested for each SNP using a chi-square test with 1 degree of freedom. Allelic odds ratio (OR) and 95% confidence interval (95% CI) were estimated based on a multiplicative model. For genotypes, a series of tests assuming an additive, dominant, or recessive genetic model were performed for each of the five SNPs using unconditional logistic regression with adjustment for age and geographic region, and the model that had the highest likelihood was considered as the best-fitting genetic model for the respective SNP.
The independent effect of each of the five previously implicated regions was tested by including the most significant SNP from each of the five regions in a logistic regression model using a backward selection procedure. Multiplicative interactions between SNPs were tested for each pair of SNPs by including both main effects and an interaction term (product of two main effects) in a logistic regression model. The cumulative effects of the five SNPs on prostate cancer were tested by counting the number of prostate cancer associated genotypes (based on the best-fitting genetic model from single SNP analysis) for these five SNPs in each subject. The OR for prostate cancer for men carrying any combination of 1, 2, 3, or ≥4 prostate cancer associated genotypes was estimated by comparing to men carrying none of the prostate cancer associated genotypes using logistic regression analysis. Tests were also performed for cumulative effect on prostate cancer association, which included five SNPs and family history.
Population attributable risk (PAR) was estimated for SNPs that remained significant after adjusting other covariates using the formula PAR=100%×p(OR−1)/[p(OR−1)+1], where p is the prevalence of prostate cancer associated genotypes among control subjects¹². The joint PAR was calculated as
$1 - (\prod_{i = 1}^{5} (1 - {PAR}_{i}))$
where PARi is the individual PAR for each associated SNP calculated under the full model and assuming no multiplicative interaction between the SNPs.
Associations of these five SNPs with TNM stages, aggressiveness of prostate cancer (advanced or localized prostate cancer), and family history (yes or no) were tested among cases only using a chi-square test of 2×N table. A trend test was used to assess the proportion of prostate cancer associated genotypes with each increasing Gleason score, from ≤4 to 10. Associations of SNPs with mean age at diagnosis were tested among cases only using a two sample t-test. Because serum PSA levels were not normally distributed, a non-parametric analysis (Wilcoxon rank sum test) was used to assess association between SNPs and pre-operative serum PSA level in cases or PSA levels at the time of sampling in controls. All reported P-values were based on a two sided test.

Results

Sixteen SNPs in five chromosomal regions (three at 8q24 and two on 17q), which were previously implicated in harboring putative genes related to susceptibility to prostate cancer were evaluated. In the control group, each SNP was in Hardy-Weinberg equilibrium (P≥0.05). Significant pair-wise linkage disequilibrium (P<0.05) was observed for the SNPs within each region.
Table 2 lists allele frequencies of the 16 SNPs among case and control subjects and shows the results of allelic and genotypic tests. Significantly different frequencies (P<0.05) between case and control subjects were observed for SNPs in each of the five chromosomal regions. At 17q12, SNP rs4430796 had the strongest association with prostate cancer; the frequency of allele ‘T’ (SNP rs4430796) was 0.61 in cases and 0.56 in controls (P=6.0×10⁻⁷). Of the four SNPs at 17q24.3, three were associated with prostate cancer, but only rs1859962 had a highly statistically significant association (P=2.1×10⁻⁴). The results for 17q12 and 17q24.3 were similar to those of a previous report⁹. For SNPs at 8q24, statistically significant associations with prostate cancer were found for all SNPs examined across the three independent regions at 8q24. Of the 16 SNPs, 13 remained significant at P<0.05 after adjusting for 16 tests using a Bonferroni correction.
Similar to the results of allelic tests, carriers of previously reported risk associated alleles for SNPs at 17q12, 17q24.3, and 8q24 were significantly more likely to be prostate cancer cases (Table 2). When various genetic models were tested for SNPs at each region, a recessive model was the best-fitting genetic model for SNPs at 17q12 and 17q24.3, and a dominant model was the best-fitting genetic model for SNPs at Regions 1, 2, and 3 of 8q24.
Due to strong genetic dependence (linkage disequilibrium) among SNPs within each region, for a combined analysis, it was possible to select one SNP (the most significant SNP from single SNP analysis) to represent each of the five regions in tests for their independent association with prostate cancer (Table 3). When these five SNPs were included in a multivariate logistic regression model, each of the five SNPs remained significantly associated with prostate cancer after adjusting for other SNPs, and each continued to be highly significant when family history was included in the model. The population attributable risks, based on adjusted ORs, for each of these five SNPs and positive family history were estimated to account for 4% to 21% of prostate cancer in this Swedish study population. The estimated joint population attributable risk for prostate cancer of the five associated SNPs plus family history was 46% in the Swedish population studied.
When multiplicative interaction was tested for each possible pair of these five SNPs using an interaction term in logistic regression, none was significant at P<0.05. However, these five SNPs appeared to have a cumulative effect on the association with prostate cancer diagnosis, adjusting for age, geographic region, and family history (Table 4). When compared with men who did not carry any prostate cancer associated genotype of these five SNPs, men that carried any combination of 1, 2, 3, or ≥4 prostate cancer associated genotypes had increasingly higher likelihood to be a prostate cancer case (P-trend=3.33×10⁻¹⁸). When family history was included as another risk factor (coded as 0 or 1) for a total of 6 possible prostate cancer associated factors, a stronger cumulative effect on prostate cancer association was observed, adjusting for age and geographic region (P-trend=3.93×10⁻²⁸). For example, compared with men who carried none of the six prostate cancer associated factors, men that carried any five or more of these associated factors had an OR of 9.48 (95% CI: 3.65-24.64, P=8.94×10⁻⁹) for prostate cancer. This cumulative effect was similarly observed in two subsets of CAPS study subjects, P-trend=1.36×10⁻¹⁰for CAPS1 and P-trend=9.03×10⁻²⁰for CAPS2
The specificity and sensitivity of the regression model was calculated by constructing receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC) statistics to estimate each model's ability to discriminate cases from control subjects. The AUC was 57.7 (95% CI: 56.0-59.3), 60.8 (59.1-62.4), and 63.3 (61.7-65.0), respectively, for the model with (1) age and region alone, (2) age, region and family history, and (3) age, region, family history and number of prostate cancer associated genotypes at the five SNPs. The AUC was significantly higher for model (3) than for model (2), P=6.12×10⁻⁶. It is important to note that these results may suffer from model over-fitting.
Table 5 shows that none of the five SNPs was significantly associated with aggressiveness of prostate cancer, Gleason score, family history, serum PSA level at diagnosis, or age at diagnosis (P>0.05). Furthermore, no associations with these clinical variables were found when multiple prostate cancer associated SNPs were considered simultaneously. For example, the 154 cases that carried four or more prostate cancer associated genotypes of these five SNPs were not significantly different from 162 cases that did not carry any prostate cancer associated genotype in terms of these clinical variables; positive family history was 17% and 21%, respectively (P=0.39), the proportion of advanced cases was 54% and 48%, respectively (P=0.33), and median serum PSA levels at diagnosis were 15 ng/ml and 14 ng/ml, respectively (P=0.27). A lack of association between these SNPs at 8q24 and clinical characteristics was also observed in previous studies^8,13,14,16, while in other studies a trend of 8q24 prostate cancer associated alleles has been reported as occurring more often in patients with higher Gleason grade, stage or aggressive disease^5-7,15,17.
Multiple chromosomal regions at 8q24 and 17q have been reported to be associated, at genome-wide significance level, with prostate cancer.^5-9While all three regions at 8q24 have been replicated in all published studies,^11,13-17no replication result has been published for regions at 17q. The highly statistically significant findings at 17q12 and 17q24.3 in this study provide the first independent confirmation for these two regions at 17q. In addition, the association of SNPs at Regions 1, 2, and 3 of 8q24 with prostate cancer was also confirmed. The discovery and confirmation of these five chromosomal regions that are associated with prostate cancer supports the value and potential of genetic association studies in complex diseases.
Although each of the SNPs in the five chromosomal regions was moderately associated with prostate cancer, the present study reveals that they have a stronger cumulative effect on prostate cancer association. It was estimated that men having 5 or more of the prostate cancer associated factors (prostate cancer associated genotypes at five SNPs and a positive family history of prostate cancer) have an odds ratio of 9.48 for prostate cancer. The cumulative effect is highly significant in the overall CAPS sample (P-trend=3.93×10⁻²⁸) and consistent between the two subsets of CAPS study subjects, P-trend=1.36×10⁻¹⁰for CAPS1 and P-trend=9.03×10⁻²⁰for CAPS2. Thus, the combined information from the five SNPs and family history can be used according to the present invention to assess an individual's risk of prostate cancer.
It was found that the presence of the five prostate cancer associated SNPs was independent of PSA levels in cases (Table 5) and controls, which suggests that some men with low PSA levels may have an increased risk of prostate cancer if they carry one or more prostate cancer associated genotypes described here. Studies using prediagnostic PSA in combination with the associated SNPs and family history will provide further insight into this aspect of the present invention.
The mechanism by which the SNPs analyzed in this study could affect the risk of prostate cancer has not been elucidated. Other than SNP rs4430796, which is located within the TCF2 gene, the specific genes affected by the rest of the SNPs have not been identified. As the five SNPs in this study appear to be associated with risk of prostate cancer in general, rather than with a more or less aggressive form, it is possible that the genetic variants described herein act at an early stage of carcinogenesis.

Example 2 Cumulative Effect of SNPs in the Five Chromosome Regions of this Invention on Prostate Cancer Risk in an African American Population

Study Population

The African American study population cases consisted of 373 prostate cancer patients undergoing treatment for prostate cancer in the Department of Urology at Johns Hopkins Hospital from 1999 to 2006. The average age at diagnosis was 57 years (median, 56 years), and the range was 36-74 years. The 372 control individuals were men undergoing disease screening and were not thought to have prostate cancer on the basis of a physical exam and a serum prostate-specific antigen (PSA) value below 4 ng/ml. Both cases and controls were self-reported African Americans (i.e., of black African ancestry). The Institutional Review Board of Johns Hopkins University approved the study protocol.

Statistical Methods

Similar statistical methods as described in Example 1 are used to assess the cumulative effect of the SNPs of this invention in the five chromosome regions described herein on prostate cancer risk in African Americans.

Results

As shown in Table 6, the risk of developing prostate cancer in African Americans increases as the number of risk genotypes of the five variants of this invention increased, in the same manner as shown for the Caucasian population described in Example 1.

Example 3. Stronger Cumulative Effect of the Five Risk Variants and Family History on Early Age of Onset Prostate Cancer

Study Population

The study population is the same Swedish population described in Example 1.

Statistical Methods

Similar statistical methods as described in Example 1 are used to assess the cumulative effect of the SNPs of this invention in the five chromosome regions described herein and family history on early age of onset of prostate cancer. Age-specific odds ratios were calculated in three intervals (<65, 65-69, >69).

Results

As shown in Table 7, ORs for prostate cancer are stronger in prostate cancer subjects with early age of onset (<65 years) than in the other groups. For example, OR was 25.94 for men with ≥5 risk factors (five risk variants and family history) among men <65 years, compared with OR of 8.27 and 4.51 among men at age 65-69 years and at age >69, respectively.
The foregoing is illustrative of the present invention, and is not to be construed as limiting thereof. The invention is defined by the following claims, with equivalents of the claims to be included therein.
All publications, patent applications, patents, patent publications, all sequences identified by GenBank® database and/or SNP accession numbers, and other references cited herein are incorporated by reference in their entireties for the sequences and/or teachings relevant to the sentence and/or paragraph and/or claim in which the reference is presented.

REFERENCES

1. Hunter D J, Kraft P. Drinking from the fire hose—statistical issues in genomewide association studies. N Engl J Med 2007; 357:436-9.
2. Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun M J. Cancer statistics, 2007. C A Cancer J Clin 2007; 57:43-66.
3. Gronberg H. Prostate cancer epidemiology. Lancet 2003; 361:859-64.
4. Johns L E, Houlston R S. A systematic review and meta-analysis of familial prostate cancer risk. BJU Int 2003; 91:789-94.
5. Amundadottir L T, Sulem P, Gudmundsson J, et al. A common variant associated with prostate cancer in European and African populations. Nat Genet 2006; 38:652-8.
6. Gudmundsson J, Sulem P, Manolescu A, et al. Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat Genet 2007; 39:631-7.
7. Haiman C A, Patterson N, Freedman M L, et al. Multiple regions within 8q24 independently affect risk for prostate cancer. Nat Genet 2007; 39:638-44.
8. Yeager M, Orr N, Hayes R B, et al. Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat Genet 2007; 39:645-9.
9. Gudmundsson J, Sulem P, Steinthorsdottir V, et al. Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet. 2007; 39:977-83.
10. Lindstrom S, Wiklund F, Adami H O, Balter K A, Adolfsson J, Gronberg H. Germ-line genetic variation in the key androgen-regulating genes androgen receptor, cytochrome P450, and steroid-5-alpha-reductase type 2 is important for prostate cancer development. Cancer Res 2006; 66:11077-83.
11. Zheng S L, Sun J, Cheng Y, et al. Association between two unlinked loci at 8q24 and prostate cancer risk among European Americans. JNCI 2007; 99:1525-1533.
12. Lillienfeld A M LDE. Foundations of epidemiology, 2^ndedition. New York: Oxford University Press, 1980; pp. 301-311.
13. Freedman M L, Haiman C A, Patterson N, et al. Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men. Proc Natl Acad Sci USA 2006; 103:14068-73.
14. Severi G, Hayes V M, Padilla E J, et al. The common variant rs1447295 on chromosome 8q24 and prostate cancer risk: results from an Australian population-based case-control study. Cancer Epidemiol Biomarkers Prev 2007; 16:610-2.
15. Wang L, McDonnell S K, Slusser J P, et al. Two common chromosome 8q24 variants are associated with increased risk for prostate cancer. Cancer Res 2007; 67:2944-50.
16. Schumacher F R, Feigelson H S, Cox D G, et al. A common 8q24 variant in prostate and breast cancer from a large nested case-control study. Cancer Res 2007; 67:2951-6.
17. Suuriniemi M, Agalliu I, Schaid D J, et al. Confirmation of a positive association between prostate cancer risk and a locus at chromosome 8q24. Cancer Epidemiol Biomarkers Prev. 2007; 16:809-14.

TABLE 1

Clinical and demographic characteristics of study subjects

# (%) of cases

# (%) of

	Aggressive	Localized	All cases	controls
Characteristics	(N = 1,231)	(N = 1,619)	(N = 2,893)	(N = 1,728)

Age at enrollment (Year)
Mean (sd)	68.04	(7.32)	65.14	(6.74)	66.36	(7.13)	67.15	(7.39)
Family History
(first-degree relatives)
No	1013	(82.29)	1295	(79.99)	2342	(80.95)	1565	(90.57)
Yes	218	(17.71)	324	(20.01)	551	(19.05)	163	(9.43)
PSA levels at diagnosis
for cases or at enrollment
for controls (ng/ml)
≤4	36	(2.95)	185	(11.61)	221	(7.85)	1439	(83.32)
5-9.99	171	(14.00)	755	(47.39)	926	(32.91)	233	(13.5)
10-19.99	216	(17.69)	438	(27.50)	654	(23.24)	38	(2.20)
20-49.99	252	(20.64)	215	(13.50)	467	(16.60)	14	(0.81)

50-99.99	229	(18.76)	0	229	(8.14)	2	(0.09)
≥100	317	(25.96)	0	317	(11.27)	1	(0.06)

Missing

10

26

79

0

Age at diagnosis (Year)

≤65	514	(41.75)	926	(57.20)	1469	(50.77)	N/A
>65	717	(58.25)	693	(42.80)	1424	(49.23)	N/A

T-stage

T0	2	(0.16)	7	(0.44)	9	(0.32)	N/A
T1	147	(12.07)	933	(58.24)	1080	(38.30)	N/A
T2	242	(19.87)	662	(41.32)	904	(32.06)	N/A

T3	724	(59.44)	0	724	(25.67)	N/A
T4	103	(8.46)	0	103	(3.65)	N/A

TX

13

17

30

N/A

N-stage

N0

222

(70.03)

302

(100.00)

524

(84.65)

N/A

N1

95

(29.97)

0

95

(15.35)

N/A

NX

914

1317

2231

N/A

M-stage

M0

589

(68.25)

655

(100.00)

1244

(81.95)

N/A

M1

274

(31.75)

0

274

(18.05)

N/A

MX

368

964

1332

N/A

Gleason (biopsy)

≤4	9	(0.83)	98	(6.32)	107	(4.06)	N/A
5	43	(3.96)	247	(15.93)	290	(10.99)	N/A
6	153	(14.08)	832	(53.64)	985	(37.34)	N/A
7	414	(38.09)	374	(24.11)	788	(29.87)	N/A

8	258	(23.74)	0	258	(9.78)	N/A
9	185	(17.02)	0	185	(7.01)	N/A

10

25

(2.30)

0

25

N/A

Missing	144	68	255	N/A

43 patients can not be classifed as aggressive or localized cases because of the missing phenotypes

TABLE 2

Association of SNPs at five chromosomal regions with prostate cancer diagnosis

Allelic tests

Chromosomal

Alternative

Associated

Frequency

SNP id	region	Position^a	alleles	allele^b	Cases	Controls	OR^c(95% CI)

rs4430796	17q12	33,172,153	T, C	T	0.61	0.56	1.24 (1.14-1.36)
rs7501939	17q12	33,175,269	G, A	G	0.66	0.62	1.22 (1.12-1.33)
rs3760511	17q12	33,180,426	A, C	C	0.41	0.38	1.17 (1.07-1.27)
rs1859962	17q24.3	66,620,348	G, T	G	0.54	0.50	1.17 (1.08-1.28)
rs7214479	17q24.3	66,702,544	C, T	T	0.50	0.48	1.08 (0.99-1.18)
rs6501455	17q24.3	66,713,406	A, G	A	0.56	0.54	1.09 (1.00-1.19)
rs983085	17q24.3	66,723,656	A, G	A	0.57	0.55	1.07 (0.98-1.16)
rs6983561	8q24 (Region 2)	128,176,062	A, C	C	0.06	0.03	1.65 (1.33-2.05)
rs16901979	8q24 (Region 2)	128,194,098	C, A	A	0.06	0.03	1.65 (1.33-2.05)
rs6983267	8q24 (Region 3)	128,482,487	G, T	G	0.56	0.51	1.22 (1.12-1.33)
rs7000448	8q24 (Region 3)	128,510,352	C, T	T	0.43	0.40	1.15 (1.06-1.25)
rs1447295	8q24 (Region 1)	128,554,220	C, A	A	0.17	0.14	1.21 (1.07-1.36)
rs4242382	8q24 (Region 1)	128,586,755	G, A	A	0.16	0.14	1.24 (1.10-1.39)
rs7017300	8q24 (Region 1)	128,594,450	T, C	C	0.20	0.18	1.15 (1.03-1.28)
rs10090154	8q24 (Region 1)	128,601,319	C, T	T	0.16	0.13	1.26 (1.11-1.42)
rs7837688	8q24 (Reqion 1)	128,608,542	G, T	T	0.15	0.13	1.17 (1.04-1.13)

Best-fitting genetic model^d

Allelic tests

Genotype^e

SNP id	P^c	Model	Reference	Associated	OR	95% CI	P^f

rs4430796	6.0E−07	Recessive	CC or TC	TT	1.40	1.23-1.59	2.68E−07
rs7501939	9.0E−06	Recessive	AA or GA	GG	1.33	1.17-1.50	5.54E−06
rs3760511	5.0E−04	Recessive	AA or CA	CC	1.42	1.20-1.68	4.47E−05
rs1859962	2.1E−04	Recessive	GT or TT	GG	1.28	1.12-1.46	3.54E−04
rs7214479	0.07	Recessive	CC or CT	TT	1.15	1.00-1.32	0.06
rs6501455	0.05	Recessive	AG or GG	AA	1.13	0.99-1.29	0.06
rs983085	0.13	Recessive	GA or GG	AA	1.11	0.97-1.26	0.12
rs6983561	4.2E−06	Dominant	AA	CA or CC	1.60	1.28-2.00	2.14E−05
rs16901979	4.3E−06	Dominant	CC	AA or CA	1.60	1.28-2.01	2.14E−05
rs6983267	3.9E−06	Dominant	TT	GT or GG	1.38	1.19-1.59	1.74E−05
rs7000448	1.4E−03	Dominant	CC	CT or TT	1.18	1.04-1.33	1.21E−02
rs1447295	1.6E−03	Dominant	CC	CA or AA	1.26	1.10-1.44	8.27E−04
rs4242382	5.3E−04	Dominant	GG	AG or AA	1.29	1.12-1.47	2.53E−04
rs7017300	0.01	Dominant	CC	CT or TT	1.20	1.05-1.36	6.20E−03
rs10090154	2.0E−04	Dominant	CC	CT or TT	1.31	1.14-1.50	1.03E−04
rs7837688	9.6E−03	Dominant	GG	GT or TT	1.21	1.06-1.39	5.87E−03

^aPosition is based on NCBI Build 35.
^bAlleles reported to be associated with prostate cancer in previously published studies (Ref 5-9, 11).
^cAllelic odds ratio is based on the multiplicative model
^dThe best-fitting model for each SNP was determined after testing associations of a series of genetic models, including dominant and recessive models, with prostate cancer in the current study
^eReference and prostate cancer associated genotypes for each SNP were defined based on the best-fitting genetic model
^fP-value is based on likelihood ratio test (1-df tests, ajusted for age and geographic region, two-sided)

TABLE 3

Adjusted OR and PAR for representative SNPs at five chromosomal regions and family history

Chromosomal

Alternative

Frequency of associated factors^b

Variables/SNPs^a	Region	alleles	Reference	Cases	Controls	b^c	OR (95% CI)	P^d	PAR (%)

Age							0.01	1.01 (1.00-1.02)	0.02
Geographic							−0.77	0.46 (0.39-0.54)	<0.0001
Family history			No	Yes	0.19	0.09	0.80	2.22 (1.83-2.68)	1.15E−17	9.89
rs4430796	17q12	T, C	CC/TC	TT	0.38	0.30	0.32	1.38 (1.21-1.57)	1.62E−06	10.23
rs1859962	17q24.3	G, T	GT/TT	GG	0.30	0.25	0.24	1.28 (1.11-1.47)	5.49E−04	6.54
rs16901979	8q24 (Region 2)	C, A	CC	AA/CA	0.10	0.07	0.42	1.53 (1.22-1.92)	1.83E−04	3.58
rs6983267	8q24 (Region 3)	G, T	TT	GT/GG	0.82	0.77	0.32	1.37 (1.18-1.59)	3.44E−05	22.17
rs1447295	8q24 (Region 1)	C, A	CC	CA/AA	0.31	0.26	0.19	1.22 (1.06-1.40)	5.31E−03	5.41

Joint-all five SNPs	40.45
Joint-all five SNPs and family history	46.34

^aFamily history and five SNPs are included in the multivariate logistic regression model adjusting for age and geographic
^bFor SNPs, the reference and prostate cancer associated genotypes at each SNP are determined based on the best-fitting model after associations of a series of genetic models with prostate cancer in the current study
^cRegression coefficient
^dBased on likelihood ratio test

TABLE 4

Cumulative effect of associated factors on prostate cancer

# of associated

# (%) of subjects

Cases vs. Controls

factors	Controls	Cases	b^e	OR	95% CI	P^f	P^g

Number of prostate cancer associated genotypes at fiveSNPs^a

Age	—	—	0.01	1.01	1.00-1.02	0.02
Geographic region	—	—	−0.76	0.46	0.40-0.55	<0.0001
Family history	—	—	0.8	2.22	1.83-2.68	7.73E−18

0 associated genotype^c	173	(10.09)	162	(5.64)	NA	1.00	—	—
1 associated genotype^c	631	(36.79)	883	(30.77)	0.41	1.50	1.18-1.92	9.46E−04
2 associated genotypes^c	618	(36.03)	1123	(39.13)	0.67	1.96	1.54-2.49	4.19E−08
3 associated genotypes^c	255	(14.87)	548	(19.09)	0.79	2.21	1.70-2.89	4.33E−09
≥4 associated genotypes^c	38	(2.22)	154	(5.37)	1.5	4.47	2.93-6.80	1.20E−13	6.75E−27

Number of prostate cancer associated factors (genotypes at fiveSNPs and family history) ^b

Age	—	—	0.01	1.01	1.00-1.02	0.02
Geographic region	—	—	−0.75	0.47	0.40-0.55	<0.0001

0 associated factor^d	174	(10.07)	144	(4.98)	NA	1.00	—	—
1 associated factor^d	581	(33.62)	778	(26.89)	0.48	1.62	1.27-2.08	1.27E−04
2 associated factors^d	622	(36.00)	1053	(36.40)	0.73	2.07	1.62-2.64	5.86E−09
3 associated factors^d	286	(16.55)	642	(22.19)	0.99	2.71	2.08-3.53	9.54E−14
4 associated factors^d	60	(3.47)	236	(8.16)	1.56	4.76	3.31-6.84	9.17E−19
≥5 associated factors^d	5	(0.29)	40	(1.38)	2.24	9.46	3.62-24.72	1.29E−08	4.78E−28

^aTesting for cumulative effect of five SNPs (rs4430796, rs1859962, rs16901979, rs6983267, and rs1447295) adjusting for age, geographic region, and family history
^bTesting for cumulative effect of the five SNPs plus family history adjusting for age and geographic region
^cNumber of prostate cancer associated genotypes at the five SNPs
^dNumber of prostate cancer associated factors (the five SNPs plus family history)
^eRegression coefficient
^fP-value is based on likelihood-ratio test, two-sided
^gP-value is based on Armitage trend test

TABLE 5

Association of five SNPs with clinical characteristics^a

rs4430796 (17q12)

rs1859962 (17q24.3)

rs16901979 (8q24)

Clinical	Reference^a	Associated^a	Reference^a	Associated^a	Reference^a
characteristics	CC/TC	TT	GT/TT	GG	CC

Number (%) of subjects at each genotype

Aggressiveness
Localized	1021	(63.50)	587	(36.50)	1113	(69.39)	491	(30.61)	1446	(90.21)
Aggressive	748	(61.46)	469	(38.54)	860	(70.78)	355	(29.22)	1077	(88.71)

P^b

0.27

0.42

Gleason score
(biopsy)
≤4	69	(65.71)	36	(34.29)	69	(65.71)	36	(34.29)	96	(91.43)
5	182	(62.98)	107	(37.02)	200	(69.44)	88	(30.56)	256	(89.51)
6	619	(63.29)	359	(36.71)	675	(69.16)	301	(30.84)	881	(90.27)
7	497	(63.64)	284	(36.36)	554	(71.21)	224	(28.79)	701	(90.10)
8	152	(59.61)	103	(40.39)	184	(72.16)	71	(27.84)	215	(84.31)
9	106	(57.61)	78	(42.39)	126	(68.48)	58	(31.52)	165	(89.67)
10	13	(52.00)	12	(48.00)	19	(76.00)	6	(24.00)	24	(96.00)

P^c

0.08

0.30

Family history
in first degree
relatives
No	1466	(63.08)	858	(36.92)	1623	(70.02)	695	(29.98)	2066	(89.17)
Yes	331	(60.85)	213	(39.15)	380	(69.85)	164	(30.15)	491	(90.42)

P^d

0.33

0.94

Mean or median at each genotype

PSA levels at
diagnosis (ng/ml)

Median

12.00

13.00

11.90

12.00

P^e

0.83

0.66

Age at
diagnosis
(Year)

Mean

65.86

65.72

65.91

65.55

65.79

P^f			0.63			0.22

rs16901979 (8q24)

rs6983267 (8q24)

rs1447295 (8q24)

Clinical	Associated^a	Reference^a	Associated^a	Reference^a	Associated^a
characteristics	AA/CA	TT	GT/GG	CC	CA/AA

Number (%) of subjects at each genotype

Aggressiveness

Localized	157	(9.79)	294	(18.41)	1303	(81.59)	1130	(70.49)	473	(29.51)
Aggressive	137	(11.29)	243	(20.03)	970	(79.97)	838	(69.03)	376	(30.97)

P^b

0.20

0.28

0.40

Gleason score
(biopsy)
≤4	9	(8.57)	22	(20.95)	83	(79.05)	80	(76.19)	25	(23.81)
5	30	(10.49)	61	(21.25)	226	(78.75)	198	(68.99)	89	(31.01)
6	95	(9.73)	170	(17.49)	802	(82.51)	697	(71.34)	280	(28.66)
7	77	(9.90)	161	(20.75)	615	(79.25)	536	(69.07)	240	(30.93)
8	40	(15.69)	47	(18.50)	207	(81.50)	179	(70.20)	76	(29.80)
9	19	(10.33)	32	(17.39)	152	(82.61)	128	(69.57)	56	(30.43)
10	1	(4.00)	8	(32.00)	17	(68.00)	18	(72.00)	7	(28.00)

P^c

0.28

0.97

0.43

Family history
in first degree
relatives
No	251	(10.83)	451	(19.50)	1862	(80.50)	1628	(70.26)	689	(29.74)
Yes	52	(9.58)	94	(17.41)	446	(82.59)	370	(68.14)	173	(31.86)

P^d

0.39

0.27

0.33

Mean or median at each genotype

PSA levels at
diagnosis (ng/ml)

Median

14.50

12.00

13.00

P^e

0.16

0.17

0.07

Age at
diagnosis
(Year)

Mean

65.84

65.76

65.80

65.85

65.67

P^f	0.91	0.90	0.54

^aReference or prostate cancer associated genotypes are determined based on the the best-fitting model at each SNP in the current study
^b,dPearson Chi-square test, two-sided
^cArmitage trend test, two-sided
^eWilcoxon rank sum test, two-sided
^fTwo-sample t test, two-sided

TABLE 6

Cummulative effect of risk factors
on prostate cancer risk in JHHAA

# of risk
factors
Number of risk

genotypes at

# (%) of subjects

Cases vs. Controls

five SNPs ^a	Controls	Cases	OR	95% CI	P ^b

0 or 1	91 (25.85)	62 (16.71)	1
2	142 (40.34)	150 (40.43)	1.55	1.04-2.30	0.03
3	3 (25.85)	121 (32.61)	1.95	1.28-2.98	0.002
≥4	28 (7.95)	38 (10.24)	1.99	1.11-3.58	0.02

^aAssuming the best-fit model at each SNP
^bP-value is based on likelihood-ratio test, two-sided

TABLE 7

Stronger cumulative effect among prostate cancer with early age of onset

	Cases	% Cases	Controls	% Controls	OR	95% L	95% U

All cases

0 risk factor	119	0.04	160	0.09	1
1 risk factor	755	0.27	573	0.34	1.77	1.35	2.3
2 risk factors	1040	0.37	619	0.36	2.28	1.75	2.96
3 risk factors	640	0.23	285	0.17	2.99	2.26	3.95
4 risk factors	234	0.08	59	0.03	5.3	3.64	7.72
>=5 risk factors	39	0.01	5	0	9.75	3.7	25.67

<65 years

0 risk factor	53	0.04	70	0.1	1
1 risk factor	351	0.27	225	0.32	2.04	1.37	3.04
2 risk factors	466	0.35	262	0.38	2.34	1.58	3.47
3 risk factors	310	0.24	113	0.16	3.53	2.31	5.39
4 risk factors	118	0.09	25	0.04	6.29	3.58	11.08
>=5 risk factors	21	0.02	1	0	25.94	3.26	200.02

65-69 years

0 risk factor	24	0.04	21	0.07	1
1 risk factor	161	0.26	113	0.36	1.21	0.6	2.45
2 risk factors	231	0.38	101	0.32	1.95	0.97	3.94
3 risk factors	143	0.23	67	0.21	1.8	0.87	3.73
4 risk factors	43	0.07	13	0.04	2.87	1.13	7.27
>=5 risk factors	10	0.02	1	0	8.27	0.9	75.66

>69 years

0 risk factor	42	0.05	69	0.1	1
1 risk factor	243	0.27	235	0.34	1.66	1.08	2.54
2 risk factors	343	0.38	256	0.37	2.16	1.42	3.29
3 risk factors	187	0.21	105	0.15	2.88	1.83	4.55
4 risk factors	73	0.08	21	0.03	5.64	3.03	10.49
>=5 risk factors	8	0.01	3	0	4.51	1.13	17.98

TABLE 8

11 SNPs with risk alleles in chromosome region
17q12; boundary from 33,163,028 to 33,189,279

CHR	SNP	POSITION	RISK ALLELE

17	rs1016990	33163028	G
17	rs3744763	33164998	A
17	rs2005705	33170413	G
17	rs757210	33170628	C
17	rs4430796	33172153	A
17	rs4239217	33173100	A
17	rs7501939	33175269	C
17	rs3760511	33180426	G
17	rs17626423	33182480	C
17	rs17626459	33185868	A
17	rs7213769	33189279	G

All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21.
Additional markers are also claimed if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table

TABLE 9

174 SNPs with risk alleles from chromosome region
17q24.3; boundary from 66,616,533 to 66,754,527

CHR	SNP	POSITION	RISK ALLELE

17	rs7222314	66616533	A
17	rs16976411	66616945	T
17	rs991528	66617179	A
17	rs17765344	66618469	A
17	rs8071558	66619268	C
17	rs8072254	66619411	A
17	rs984434	66619722	T
17	rs1859962	66620348	G
17	rs11650165	66621213	C
17	rs991429	66621368	G
17	rs4793528	66622368	A
17	rs9674957	66622693	G
17	rs8077906	66623828	G
17	rs8066875	66625172	A
17	rs9889335	66626741	T
17	rs4328484	66627825	G
17	rs8068266	66628530	A
17	rs12947919	66629682	T
17	rs4793529	66630231	T
17	rs7217652	66631076	T
17	rs6501437	66631567	G
17	rs6501438	66631755	A
17	rs8079315	66632450	C
17	rs2367256	66632881	A
17	rs2190697	66632936	A
17	rs4366746	66633226	G
17	rs4366747	66633238	G
17	rs2159034	66633350	C
17	rs1013999	66633530	C
17	rs4793530	66636858	C
17	rs11654749	66637201	G
17	rs11653132	66641427	G
17	rs4300694	66642431	T
17	rs8076830	66643504	T
17	rs9900242	66647226	G
17	rs9908442	66649543	G
17	rs4793334	66649577	A
17	rs2058083	66649998	C
17	rs2058084	66650612	C
17	rs2058085	66650642	A
17	rs1468481	66651574	C
17	rs9915190	66654223	C
17	rs8065751	66654373	T
17	rs8080251	66654402	A
17	rs17178083	66654469	G
17	rs2041114	66656212	A
17	rs723338	66657008	A
17	rs2041115	66658022	A
17	rs8064263	66658425	A
17	rs9897865	66658671	C
17	rs11656242	66659117	A
17	rs9897358	66659137	G
17	rs11651123	66659186	G
17	rs11657298	66659231	T
17	rs11651469	66660114	T
17	rs11651501	66660153	T
17	rs719615	66661505	G
17	rs7219299	66662350	G
17	rs9916274	66663128	C
17	rs7209594	66663375	T
17	rs1558119	66663567	C
17	rs12150098	66667429	T
17	rs17824720	66668449	T
17	rs9747823	66669172	C
17	rs9910829	66671172	A
17	rs7220274	66671362	A
17	rs17224833	66672070	A
17	rs2108534	66672495	A
17	rs2108535	66672740	T
17	rs8182284	66672986	T
17	rs8182286	66673149	T
17	rs4793533	66676066	T
17	rs8069925	66676457	A
17	rs8068189	66676490	G
17	rs9901508	66676794	A
17	rs9907418	66676814	T
17	rs2367263	66677883	G
17	rs1859964	66678166	T
17	rs1859965	66678690	C
17	rs6501446	66679653	T
17	rs4793534	66679888	C
17	rs4239156	66679976	T
17	rs4793335	66680286	G
17	rs2108536	66681371	G
17	rs7216882	66682565	A
17	rs2097984	66682821	T
17	rs11654068	66684131	C
17	rs8079962	66684297	A
17	rs6501447	66684693	T
17	rs7206969	66684744	G
17	rs2886914	66685408	C
17	rs9909797	66686777	G
17	rs8076811	66687002	A
17	rs1859966	66687338	C
17	rs17178251	66688474	C
17	rs7211425	66691073	G
17	rs17765644	66691087	T
17	rs9913988	66691653	T
17	rs11871129	66692057	C
17	rs758106	66692598	T
17	rs740408	66692691	A
17	rs17224938	66694338	T
17	rs17824822	66695549	T
17	rs4570900	66697961	G
17	rs1011729	66699182	C
17	rs1011730	66699340	A
17	rs16976453	66700323	A
17	rs4611499	66700364	T
17	rs6501448	66701508	G
17	rs7208398	66702451	T
17	rs7214479	66702544	T
17	rs1008348	66702911	G
17	rs2367265	66702962	C
17	rs6501449	66704440	C
17	rs6501451	66704726	T
17	rs6501452	66704882	G
17	rs8079118	66705375	G
17	rs11870732	66706836	A
17	rs17178370	66707136	T
17	rs17225050	66709084	T
17	rs7225025	66709269	A
17	rs2215050	66709704	A
17	rs17178377	66709728	G
17	rs11655744	66710651	T
17	rs2367266	66711582	G
17	rs1107305	66712238	G
17	rs6501454	66713352	T
17	rs6501455	66713406	A
17	rs7209505	66715259	A
17	rs7209069	66715391	T
17	rs13342783	66717627	G
17	rs8067671	66718759	C
17	rs2190463	66719063	T
17	rs2190456	66722961	C
17	rs983084	66723461	G
17	rs983085	66723656	A
17	rs6501459	66725050	C
17	rs4793538	66727523	C
17	rs2158905	66727636	C
17	rs2190457	66728004	T
17	rs11655567	66728282	T
17	rs7225458	66729941	A
17	rs10401004	66730351	A
17	rs7215164	66731916	G
17	rs917278	66733520	C
17	rs1978203	66734264	T
17	rs1978204	66734540	G
17	rs737956	66735463	G
17	rs737957	66735504	A
17	rs8075481	66735791	C
17	rs8080004	66735894	T
17	rs7224058	66737374	G
17	rs7215307	66737962	T
17	rs4793541	66739190	T
17	rs7221080	66741567	G
17	rs8064388	66742612	G
17	rs8076167	66744678	T
17	rs8067695	66745906	T
17	rs9898561	66746448	G
17	rs9906756	66747639	A
17	rs17178530	66747707	G
17	rs17765886	66747800	C
17	rs12946942	66748593	T
17	rs16976482	66749123	C
17	rs9302933	66750402	G
17	rs9914509	66750867	G
17	rs9895657	66751026	C
17	rs12941471	66751530	A
17	rs9896822	66751706	A
17	rs8070461	66752467	G
17	rs2214946	66753475	T
17	rs9909596	66753588	G
17	rs16976490	66753642	T
17	rs9891216	66754527	C

All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21.
Additional markers are also to be included if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table.

TABLE 10

116 SNPs with risk alleles from chromosome region 8q24
(region 1); boundary from 128,536,936 to 128,617,860

CHR	SNP	POSITION	RISK ALLELE

8	rs7017671	128536936	C
8	rs10099905	128537116	A
8	rs10956372	128539438	T
8	rs7830412	128540223	A
8	rs7387447	128540858	C
8	rs10094871	128541151	A
8	rs1447293	128541502	C
8	rs921146	128544367	G
8	rs7825118	128544999	A
8	rs2121630	128547342	T
8	rs3999775	128548719	T
8	rs4871798	128549145	T
8	rs10089310	128550166	T
8	rs7819102	128550531	C
8	rs4871799	128551824	G
8	rs6981424	128552278	G
8	rs6470519	128553405	A
8	rs7818556	128553581	G
8	rs1447295	128554220	A
8	rs10109700	128555146	A
8	rs9297758	128555770	G
8	rs13363309	128556704	G
8	rs13259396	128557893	A
8	rs13260378	128557932	G
8	rs7826179	128558381	T
8	rs10956373	128559758	C
8	rs7836840	128560974	T
8	rs7831028	128561211	C
8	rs1992833	128561526	T
8	rs2290033	128562256	G
8	rs9643225	128563573	T
8	rs9643226	128563663	C
8	rs11775749	128563848	A
8	rs11994384	128564509	G
8	rs1447296	128564541	T
8	rs16902168	128564790	T
8	rs9643227	128565278	C
8	rs16902169	128565688	A
8	rs13253127	128565773	T
8	rs6985504	128565958	A
8	rs13258548	128566029	A
8	rs13258812	128566049	C
8	rs16902172	128567224	G
8	rs1447297	128567804	C
8	rs7831150	128568620	G
8	rs723555	128569281	G
8	rs10808558	128570332	A
8	rs10103005	128571003	G
8	rs7820229	128571765	C
8	rs16902173	128573181	A
8	rs17766217	128573679	T
8	rs4871806	128574318	C
8	rs12155672	128576206	A
8	rs12156128	128576373	C
8	rs1562434	128576501	C
8	rs1562433	128576632	G
8	rs1562432	128576784	T
8	rs1562431	128576833	C
8	rs12056473	128577104	A
8	rs12056788	128577254	G
8	rs4599773	128579606	C
8	rs4078240	128580745	A
8	rs6981321	128582487	C
8	rs4871808	128582727	C
8	rs7832031	128586134	A
8	rs4242382	128586755	A
8	rs4242383	128586942	A
8	rs4314621	128587197	G
8	rs4242384	128587736	C
8	rs7018386	128589139	C
8	rs7812429	128589355	A
8	rs7812894	128589661	A
8	rs4871026	128589959	C
8	rs4871027	128590689	G
8	rs10099413	128591245	T
8	rs7814837	128591384	T
8	rs10088308	128592096	C
8	rs9297760	128592354	A
8	rs7007540	128592822	A
8	rs7824868	128593596	T
8	rs7017300	128594450	C
8	rs12547874	128594814	A
8	rs6470526	128595073	G
8	rs7004374	128595167	T
8	rs7005343	128595760	A
8	rs9693113	128596612	C
8	rs4871809	128596737	T
8	rs7461151	128596912	A
8	rs6470527	128597013	A
8	rs6470528	128597549	A
8	rs4582524	128597617	G
8	rs4498506	128598215	A
8	rs4297007	128598298	G
8	rs4242385	128598411	G
8	rs11992171	128599115	C
8	rs13255059	128599798	A
8	rs13265719	128600210	T
8	rs11986220	128600871	A
8	rs11988857	128601055	G
8	rs10090154	128601319	T
8	rs10103849	128601549	G
8	rs7824776	128602624	C
8	rs7843031	128602655	T
8	rs4531012	128603543	G
8	rs9656967	128603769	T
8	rs9656816	128603836	G
8	rs12548153	128603874	T
8	rs12542685	128606765	A
8	rs7814251	128607399	C
8	rs9694093	128608330	G
8	rs7837688	128608542	T
8	rs7825823	128611099	C
8	rs6991990	128614565	C
8	rs11988207	128616342	A
8	rs12386846	128617631	T
8	rs13258742	128617860	G

All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21.
Additional markers are also to be included if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table.

TABLE 11

112 SNPs with risk alleles from chromosome region 8q24
(region 2); boundary from 128,469,358 to 128,535,996

CHR	SNP	POSITION	RISK ALLELE

8	rs3940781	128145397	A
8	rs16901935	128145727	A
8	rs12542102	128146703	T
8	rs11988135	128148591	T
8	rs2392727	128148778	A
8	rs2392728	128148825	G
8	rs2392729	128148848	T
8	rs1014656	128148865	T
8	rs2392730	128148876	A
8	rs2392731	128148922	T
8	rs2392732	128149116	A
8	rs2893603	128149139	T
8	rs9656965	128149430	C
8	rs17831626	128149605	T
8	rs16901938	128149682	A
8	rs7824679	128150292	A
8	rs7824923	128150320	T
8	rs7843300	128150413	C
8	rs7824957	128150442	A
8	rs7825414	128150703	G
8	rs13282364	128151591	A
8	rs13363429	128152031	A
8	rs6993569	128153279	G
8	rs6994316	128153721	G
8	rs12550334	128154106	G
8	rs11998124	128154662	C
8	rs6999589	128154828	A
8	rs11998248	128154937	G
8	rs1902431	128156258	T
8	rs6470494	128157086	T
8	rs1016342	128161637	T
8	rs1551511	128161996	G
8	rs1031588	128162459	A
8	rs1016343	128162479	T
8	rs1551510	128162660	T
8	rs4871008	128162723	C
8	rs6981122	128163642	C
8	rs13252298	128164338	A
8	rs7841060	128165659	G
8	rs7007694	128168348	C
8	rs4571699	128168451	A
8	rs1840709	128168637	G
8	rs11993508	128169258	C
8	rs17832021	128169358	A
8	rs3857883	128169788	G
8	rs1456316	128170030	A
8	rs16901946	128170107	G
8	rs9656813	128170146	A
8	rs9656814	128170159	C
8	rs10505484	128170494	G
8	rs7844454	128171683	T
8	rs12682421	128172333	G
8	rs1456315	128173119	T
8	rs13254738	128173525	C
8	rs12682344	128175966	G
8	rs6983561	128176062	C
8	rs16901948	128176283	A
8	rs1869931	128176318	A
8	rs16901949	128176335	C
8	rs16901950	128176425	A
8	rs16901952	128176452	C
8	rs16901953	128177411	C
8	rs7010450	128177861	G
8	rs6990420	128177907	T
8	rs17832285	128178175	A
8	rs7825340	128178311	G
8	rs12544977	128178469	A
8	rs16901959	128178712	G
8	rs7826337	128178756	G
8	rs7826388	128178958	T
8	rs7830341	128179112	A
8	rs16901966	128179434	G
8	rs16901967	128179459	G
8	rs7000910	128179787	A
8	rs7001069	128179828	G
8	rs17765137	128179996	A
8	rs11781162	128180078	A
8	rs11774449	128180214	T
8	rs7006409	128180611	G
8	rs6988257	128180638	C
8	rs16901969	128181279	C
8	rs16901970	128181897	G
8	rs10453084	128181961	A
8	rs6987723	128182041	A
8	rs6987640	128182210	T
8	rs3956788	128183074	C
8	rs7824451	128183643	G
8	rs7824785	128183892	T
8	rs13268116	128183978	G
8	rs6470498	128184902	G
8	rs1011829	128184925	A
8	rs1456306	128185682	G
8	rs7844219	128187997	G
8	rs1378897	128191841	T
8	rs1551512	128193308	G
8	rs16901979	128194098	A
8	rs10505483	128194377	T
8	rs7817677	128194686	G
8	rs10505482	128195031	A
8	rs1456305	128196434	A
8	rs17184796	128197641	T
8	rs16901983	128198224	T
8	rs6989838	128198554	C
8	rs7013255	128199669	G
8	rs16901984	128200143	C
8	rs10098156	128200973	G
8	rs6995291	128201506	A
8	rs16901985	128203159	A
8	rs7824364	128204547	C
8	rs7816535	128206850	A
8	rs16901988	128207742	C
8	rs6470500	128215780	A

All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21.
Additional markers are also to be included if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table.

TABLE 12

70 SNPs with risk alleles from chromosome region 8q24
(Region 3); boundary from 128,469,358 to 128,535,996

CHR	SNP	POSITION	RISK ALLELE

8	rs7820981	128469358	C
8	rs1562871	128470954	T
8	rs12549845	128472089	G
8	rs10441525	128472135	C
8	rs7844673	128472696	G
8	rs10956365	128473069	A
8	rs16902147	128474254	C
8	rs16902148	128476181	A
8	rs16902149	128476287	C
8	rs3847136	128476372	A
8	rs10505477	128476625	A
8	rs12334317	128477246	C
8	rs10505476	128477298	T
8	rs11985829	128478414	T
8	rs10808555	128478693	G
8	rs10505475	128480639	G
8	rs17467139	128481192	G
8	rs10808556	128482329	C
8	rs6983267	128482487	G
8	rs3847137	128483680	C
8	rs7013278	128484074	T
8	rs10505474	128486686	T
8	rs10505473	128487118	T
8	rs11986916	128488689	C
8	rs10098876	128489098	G
8	rs2060776	128489299	G
8	rs13248944	128489740	C
8	rs4871788	128490967	G
8	rs7837328	128492309	A
8	rs7837626	128492523	A
8	rs7837644	128492580	A
8	rs10956368	128492832	T
8	rs10956369	128492999	T
8	rs7014346	128493974	A
8	rs871135	128495575	G
8	rs6985419	128498903	T
8	rs7842552	128500876	G
8	rs12375310	128501388	A
8	rs7005829	128504126	T
8	rs1447294	128506868	T
8	rs9297756	128509349	A
8	rs6995633	128509833	A
8	rs6999789	128510043	C
8	rs6999921	128510110	G
8	rs7000448	128510352	T
8	rs6982665	128510403	C
8	rs7357486	128510805	T
8	rs7357368	128512569	T
8	rs7829370	128515566	T
8	rs12334463	128518887	C
8	rs13280578	128519269	A
8	rs6470512	128519904	T
8	rs7007536	128520151	G
8	rs10090421	128522947	G
8	rs12334695	128523110	C
8	rs6981397	128524836	A
8	rs7831606	128524876	A
8	rs10101741	128525201	A
8	rs7012462	128526872	T
8	rs10109622	128527333	T
8	rs10109723	128527420	G
8	rs6996874	128527491	G
8	rs4871791	128527826	C
8	rs13282506	128528307	G
8	rs6470517	128529586	A
8	rs7841228	128530060	G
8	rs10094059	128530789	G
8	rs11781420	128534524	A
8	rs9643221	128534669	G
8	rs7841264	128535996	C

All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21.
Additional markers are also to be included if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table.

TABLE 13

WELL	TERM	SNP_ID	2nd-PCRP	1st-PCRP	UEP_SEQ

W1	iPLEX	rs4645959	ACGTTGGATGTCGTCGCAGTAGAAATACGG	ACGTTGGATGTGCCCCTCAACGTTAGCTTC	CGTAGTCGAGGTCATAG

W1	iPLEX	rs1668875	ACGTTGGATGAGTCATCCTGAGTACTCAGC	ACGTTGGATGTGCCCAGATATGAGAGTGAG	TACTCAGCATTCCCCAAAA

W1	iPLEX	rs12334695	ACGTTGGATGTGTGTGTGCACATGTGCTTG	ACGTTGGATGCAGCAGAGCTCCATGAAAAG	CCACTCTCTCTATTCCCCTC

W1	iPLEX	rs7824074	ACGTTGGATGTTCATCCACACTCCCATCTC	ACGTTGGATGAAGAGGAAGACTGGGAAAGG	AGTTCCTGTTCACAACCAAG

W1	iPLEX	rs10086908	ACGTTGGATGTTAGATGCCCCTTCTGTGTG	ACGTTGGATGGGAAATTACACTTCATGATG	atagCACCTCAAACTTCCCCT

W1	iPLEX	rs6983267	ACGTTGGATGTCATCGTCCTTTGAGCTCAG	ACGTTGGATGCTCCCTCCCCCACATAAAAT	ttttAGCTCAGCAGATGAAAG

W1	iPLEX	rs7837688	ACGTTGGATGTGACGTGTCAACATAGACCC	ACGTTGGATGTTCACAGCCTCCCTCATTAC	ccCAACATAGACCCAATTGTAC

W1	iPLEX	rs16901979	ACGTTGGATGAGTTCAGTTCACTTTCTTCC	ACGTTGGATGGTTGTGGAGCAGTGTTAATG	CTCAAAAATACCATTTGCCAGA

W1	iPLEX	rs10505473	ACGTTGGATGACGTTGACTCCTTAGAATGG	ACGTTGGATGTCAGGTTCGTAACCTTGGTG	ATGGTTGAAATGGTAGTATTCCA

W1	iPLEX	rs7841193	ACGTTGGATGTCCTGCCTCTTTTCCTTCAC	ACGTTGGATGGGATATGGGATAGGCTTGAG	ctTAGGACAATACCTCACCTACAT

W1	iPLEX	rs12375310	ACGTTGGATGCTGCTATGAAACCACTGTCC	ACGTTGGATGGCTGGGAGATTAAAACAAAC	TCCACTGATTATTTTTTTGTGTTT

W1	iPLEX	rs7017300	ACGTTGGATGGACCATGAACAATGAGATTCG	ACGTTGGATGAAATCACTGCAACTGCCCTG	ggtgTCCCTTTGTATGATGCCTAGA

W1	iPLEX	rs6470572	ACGTTGGATGACTCAGTCACTCCAGGGACA	ACGTTGGATGGGCCAGACTTTGAATCTTAC	ccatGGACAGGCACCAGAAGAGATG

W1	iPLEX	rs6981122	ACGTTGGATGGGTCCTTCATCCCATTCTTG	ACGTTGGATGAGTTAACAGCAGCCGATATG	aactACCTCCTAAAGAACCTACTATT

W2	iPLEX	rs4242382	ACGTTGGATGCAGGGAACATTTTGTCCCTC	ACGTTGGATGGTGTTCCTAGGTTCTCTGTG	CCCTCTAGTTATCTTCCC

W2	iPLEX	rs1447295	ACGTTGGATGCTACCCCCACCAGCATTTTT	ACGTTGGATGATTGAGGAAGTGCCATTGGG	tTTGCTTTTTTTCCATAGCAC

W2	iPLEX	rs1467191	ACGTTGGATGTTGGGCAACCCCAACCTTAG	ACGTTGGATGGGATTAAACATGTGGTGCTG	ACCCCAACCTTAGATCTTCTTTC

W2	iPLEX	rs622556	ACGTTGGATGACCCATACTCAGCCTTTACC	ACGTTGGATGCACATGTTTTCTTAGGATAG	tcGTTGAATTATCATCAACAGCTT

Claims

1.-19. (canceled)

20. A method of measuring a prostate specific antigen (PSA) level in a subject, comprising:

(a) obtaining a nucleic acid sample from the subject;

(b) detecting in the nucleic acid sample from the subject a plurality of alleles consisting of:

(1) the T allele of single nucleotide polymorphism rs4430796;

(2) the G allele of single nucleotide polymorphism rs1859962;

(3) the A allele of single nucleotide polymorphism rs16901979;

(4) the G allele of single nucleotide polymorphism rs6983267; and

(5) the A allele of single nucleotide polymorphism rs1447295,

by an amplification reaction, hybridization, restriction endonuclease digestion and/or electrophoresis, and

(c) measuring the PSA level in said subject having each of the alleles (1)-(5).

21. A method of measuring a prostate specific antigen (PSA) level in a subject, consisting essentially of:

(a) obtaining a nucleic acid sample from the subject;

(1) the T allele of single nucleotide polymorphism rs4430796;

(2) the G allele of single nucleotide polymorphism rs1859962;

(3) the A allele of single nucleotide polymorphism rs16901979;

(4) the G allele of single nucleotide polymorphism rs6983267; and

(5) the A allele of single nucleotide polymorphism rs1447295,

(c) measuring the PSA level in said subject based on the subject having each of the alleles (1)-(5).

22. A method of detecting a plurality of alleles in a subject consisting of:

(1) the T allele of single nucleotide polymorphism rs4430796;

(2) the G allele of single nucleotide polymorphism rs1859962;

(3) the A allele of single nucleotide polymorphism rs16901979;

(4) the G allele of single nucleotide polymorphism rs6983267; and

(5) the A allele of single nucleotide polymorphism rs1447295,

consisting of:

(a) obtaining a nucleic acid sample from the subject; and

(b) detecting the alleles (1)-(5) in the nucleic acid sample by contacting the nucleic acid sample with oligonucleotides that hybridize to respective nucleotide sequences comprising each of the alleles (1)-(5), and detecting hybridization between the respective nucleotide sequences and the oligonucleotides.

23. The method of claim 20, wherein the subject has a family history of prostate cancer.

24. The method of claim 21, wherein the subject has a family history of prostate cancer.

25. The method of claim 22, wherein the subject has a family history of prostate cancer.

26. A method of diagnosing prostate cancer in a subject, comprising

(a) obtaining a nucleic acid sample from the subject;

(1) the T allele of single nucleotide polymorphism rs4430796;

(2) the G allele of single nucleotide polymorphism rs1859962;

(3) the A allele of single nucleotide polymorphism rs16901979;

(4) the G allele of single nucleotide polymorphism rs6983267; and

(5) the A allele of single nucleotide polymorphism rs1447295,

by an amplification reaction, hybridization, restriction endonuclease digestion and/or electrophoresis;

(c) measuring the PSA level in the subject having each allele (1)-(5); and

(d) diagnosing the subject as having prostate cancer based on the measured PSA level of the subject and the presence of each allele (1)-(5).

27. The method of claim 26, wherein the subject has a family history of prostate cancer.

28. A method of identifying a subject as having an increased risk of developing prostate cancer, comprising:

(a) obtaining a nucleic acid sample from the subject;

(1) the T allele of single nucleotide polymorphism rs4430796;

(2) the G allele of single nucleotide polymorphism rs1859962;

(3) the A allele of single nucleotide polymorphism rs16901979;

(4) the G allele of single nucleotide polymorphism rs6983267; and

(5) the A allele of single nucleotide polymorphism rs1447295,

(c) measuring the PSA level in a subject having each allele (1)-(5); and

(d) identifying the subject as having an increased risk of developing prostate cancer based on the measured PSA level of the subject and the presence of each allele (1)-(5).

29. The method of claim 28, wherein the subject has a family history of prostate cancer.

30. The method of claim 23, wherein detection of all five SNPs in the nucleic acid sample from the subject and a positive family history of prostate cancer for the subject indicates an odds ratio for prostate cancer of 9.46 or greater.

31. The method of claim 24, wherein detection of all five SNPs in the nucleic acid sample from the subject and a positive family history of prostate cancer for the subject indicates an odds ratio for prostate cancer of 9.46 or greater.

32. The method of claim 25, wherein detection of all five SNPs in the nucleic acid sample from the subject and a positive family history of prostate cancer for the subject indicates an odds ratio for prostate cancer of 9.46 or greater.

33. The method of claim 27, wherein detection of all five SNPs in the nucleic acid sample from the subject and a positive family history of prostate cancer for the subject indicates an odds ratio for prostate cancer of 9.46 or greater.

34. The method of claim 29, wherein detection of all five SNPs in the nucleic acid sample from the subject and a positive family history of prostate cancer for the subject indicates an odds ratio for prostate cancer of 9.46 or greater.